Resources · Blog
Research, benchmarks, and lessons from deploying agentic AI on real production lines.
Traditional vector-search RAG breaks down on complex enterprise questions. This piece introduces Enhanced RAG — knowledge-graph-aware retrieval, query rewriting, multi-hop reasoning, hybrid reranking, and vision-aware analysis of technical figures — so retrieval works on meaning and intent rather than surface similarity.
ReadHigh character-accuracy scores hide the fact that a tiny OCR error can wreck a document QA pipeline. ThirdAI's Beyond OCR argues that messy industrial documents need structure-aware extraction, on-prem processing, and smart chunking instead of 'good enough' character recognition.
ReadThirdAI introduces TIA-pdf-QA-Bench, a benchmark that evaluates end-to-end question answering over messy, complex industrial PDFs rather than isolated OCR accuracy. It shows how OCR, chunking, and retrieval choices shape QA quality — and where ThirdAI's RAG framework leads.
ReadTraditional defect detection tells fabs a wafer is bad but not why. ThirdAI Automation's probabilistic contrastive counterfactuals generate quantified "what if" scenarios that pinpoint which process parameters drive defects and how much adjusting them raises yield.
ReadWhen experienced engineers leave, their hard-won tacit knowledge walks out with them - driving inefficient maintenance, longer troubleshooting, and costly downtime. This piece quantifies the per-hour cost across industries and lays out strategies for retaining critical expertise.
ReadTraditional root cause analysis is undermined by unstructured documentation scattered across emails, chats, and ad hoc notes. This article shows how LLMs and AI agents extract, integrate, and learn from that data to make RCA faster, more consistent, and continuously improving.
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It models cause and effect across your operational knowledge graph, so you get the true driver with the evidence behind it - not just a correlated signal that leaves engineers guessing.
A typical pilot runs 60-90 days on your own operational data, with measurable impact on RCA cycle time by the end of the engagement.
Deploy in your VPC or fully on-prem / air-gapped - data never leaves your environment. We hold SOC 2 Type II, ISO 27001, and CCPA.
MES, SPC/FDC, historians, EDA & yield, PLM, CMMS, and ticketing - all feed the CIP Core evidence layer through standard connectors.
No. Enterprise MLOps governs fab-specific models for you, so engineers work inside the resolution workflow instead of notebooks.
See the Causal Intelligence Platform (CIP) run on your own operational data. One causal engine and one knowledge graph connect diagnostics, process intelligence, defect feedback, and resolution orchestration - so every finding is explained, scored, and acted on.
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